
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import itertools


app=pd.read_csv("application_data.csv")
prev=pd.read_csv("previous_application.csv")
col=pd.read_csv("columns_description.csv",encoding='ISO-8859-1')
# The file I'm trying to read might not actually be encoded in utf-8. It could be using another encoding like ISO-8859-1


app.head()


prev.head()


col.head()


app.describe()


prev.describe()


col.describe()


app.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


prev.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


col.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 160 entries, 0 to 159
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   Unnamed: 0   160 non-null    int64 
 1   Table        160 non-null    object
 2   Row          160 non-null    object
 3   Description  160 non-null    object
 4   Special      69 non-null     object
dtypes: int64(1), object(4)
memory usage: 6.4+ KB


app.shape

(307511, 122)


prev.shape

(1670214, 37)


col.shape

(160, 5)


col.isnull().sum()

Unnamed: 0      0
Table           0
Row             0
Description     0
Special        91
dtype: int64


prev.isnull().sum()

SK_ID_PREV                           0
SK_ID_CURR                           0
NAME_CONTRACT_TYPE                   0
AMT_ANNUITY                     372235
AMT_APPLICATION                      0
AMT_CREDIT                           1
AMT_DOWN_PAYMENT                895844
AMT_GOODS_PRICE                 385515
WEEKDAY_APPR_PROCESS_START           0
HOUR_APPR_PROCESS_START              0
FLAG_LAST_APPL_PER_CONTRACT          0
NFLAG_LAST_APPL_IN_DAY               0
RATE_DOWN_PAYMENT               895844
RATE_INTEREST_PRIMARY          1664263
RATE_INTEREST_PRIVILEGED       1664263
NAME_CASH_LOAN_PURPOSE               0
NAME_CONTRACT_STATUS                 0
DAYS_DECISION                        0
NAME_PAYMENT_TYPE                    0
CODE_REJECT_REASON                   0
NAME_TYPE_SUITE                 820405
NAME_CLIENT_TYPE                     0
NAME_GOODS_CATEGORY                  0
NAME_PORTFOLIO                       0
NAME_PRODUCT_TYPE                    0
CHANNEL_TYPE                         0
SELLERPLACE_AREA                     0
NAME_SELLER_INDUSTRY                 0
CNT_PAYMENT                     372230
NAME_YIELD_GROUP                     0
PRODUCT_COMBINATION                346
DAYS_FIRST_DRAWING              673065
DAYS_FIRST_DUE                  673065
DAYS_LAST_DUE_1ST_VERSION       673065
DAYS_LAST_DUE                   673065
DAYS_TERMINATION                673065
NFLAG_INSURED_ON_APPROVAL       673065
dtype: int64


# Calculate percentage of missing values
miss_previous_application = pd.DataFrame({
    'column': prev.columns,
    'missing_percentage': (prev.isnull().sum() * 100) / prev.shape[0]
}).reset_index(drop=True)

# Add a type column
miss_previous_application["type"] = "previous_application"

# Plot
fig = plt.figure(figsize=(18, 6))
ax = sns.pointplot(x="column", y="missing_percentage", data=miss_previous_application, hue="type")
plt.xticks(rotation=90, fontsize=7)
plt.title("Percentage of Missing Values in previous_application")
plt.ylabel("Percentage")
plt.xlabel("Columns")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
plt.show()


round(100*(prev.isnull().sum()/len(prev.index)),2)

SK_ID_PREV                      0.00
SK_ID_CURR                      0.00
NAME_CONTRACT_TYPE              0.00
AMT_ANNUITY                    22.29
AMT_APPLICATION                 0.00
AMT_CREDIT                      0.00
AMT_DOWN_PAYMENT               53.64
AMT_GOODS_PRICE                23.08
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
FLAG_LAST_APPL_PER_CONTRACT     0.00
NFLAG_LAST_APPL_IN_DAY          0.00
RATE_DOWN_PAYMENT              53.64
RATE_INTEREST_PRIMARY          99.64
RATE_INTEREST_PRIVILEGED       99.64
NAME_CASH_LOAN_PURPOSE          0.00
NAME_CONTRACT_STATUS            0.00
DAYS_DECISION                   0.00
NAME_PAYMENT_TYPE               0.00
CODE_REJECT_REASON              0.00
NAME_TYPE_SUITE                49.12
NAME_CLIENT_TYPE                0.00
NAME_GOODS_CATEGORY             0.00
NAME_PORTFOLIO                  0.00
NAME_PRODUCT_TYPE               0.00
CHANNEL_TYPE                    0.00
SELLERPLACE_AREA                0.00
NAME_SELLER_INDUSTRY            0.00
CNT_PAYMENT                    22.29
NAME_YIELD_GROUP                0.00
PRODUCT_COMBINATION             0.02
DAYS_FIRST_DRAWING             40.30
DAYS_FIRST_DUE                 40.30
DAYS_LAST_DUE_1ST_VERSION      40.30
DAYS_LAST_DUE                  40.30
DAYS_TERMINATION               40.30
NFLAG_INSURED_ON_APPROVAL      40.30
dtype: float64


previous_application=prev.drop([ 'AMT_DOWN_PAYMENT', 'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       "RATE_INTEREST_PRIVILEGED"],axis=1)


# Calculate the percentage of missing values for each column
miss_previous_application = pd.DataFrame({
    'column': previous_application.columns,
    'missing_percentage': (previous_application.isnull().sum() * 100) / previous_application.shape[0]
}).reset_index(drop=True)

# Add a "type" column for plotting
miss_previous_application["type"] = "previous_application"

# Create the plot
fig = plt.figure(figsize=(18, 6))
ax = sns.pointplot(x="column", y="missing_percentage", data=miss_previous_application, hue="type")
plt.xticks(rotation=90, fontsize=7)
plt.title("Percentage of Missing Values in previous_application")
plt.ylabel("Percentage")
plt.xlabel("Columns")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
plt.show()


round(100*(previous_application.isnull().sum()/len(previous_application.index)),2)

SK_ID_PREV                      0.00
SK_ID_CURR                      0.00
NAME_CONTRACT_TYPE              0.00
AMT_ANNUITY                    22.29
AMT_APPLICATION                 0.00
AMT_CREDIT                      0.00
AMT_GOODS_PRICE                23.08
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
FLAG_LAST_APPL_PER_CONTRACT     0.00
NFLAG_LAST_APPL_IN_DAY          0.00
NAME_CASH_LOAN_PURPOSE          0.00
NAME_CONTRACT_STATUS            0.00
DAYS_DECISION                   0.00
NAME_PAYMENT_TYPE               0.00
CODE_REJECT_REASON              0.00
NAME_TYPE_SUITE                49.12
NAME_CLIENT_TYPE                0.00
NAME_GOODS_CATEGORY             0.00
NAME_PORTFOLIO                  0.00
NAME_PRODUCT_TYPE               0.00
CHANNEL_TYPE                    0.00
SELLERPLACE_AREA                0.00
NAME_SELLER_INDUSTRY            0.00
CNT_PAYMENT                    22.29
NAME_YIELD_GROUP                0.00
PRODUCT_COMBINATION             0.02
DAYS_FIRST_DRAWING             40.30
DAYS_FIRST_DUE                 40.30
DAYS_LAST_DUE_1ST_VERSION      40.30
DAYS_LAST_DUE                  40.30
DAYS_TERMINATION               40.30
NFLAG_INSURED_ON_APPROVAL      40.30
dtype: float64


print("AMT_ANNUITY NULL COUNT:" ,previous_application['AMT_ANNUITY'].isnull().sum())

AMT_ANNUITY NULL COUNT: 372235


previous_application['AMT_ANNUITY'].describe()

count    1.297979e+06
mean     1.595512e+04
std      1.478214e+04
min      0.000000e+00
25%      6.321780e+03
50%      1.125000e+04
75%      2.065842e+04
max      4.180581e+05
Name: AMT_ANNUITY, dtype: float64


sns.set_style('whitegrid') 
sns.distplot(previous_application['AMT_ANNUITY']) 
plt.show()


print("AMT_GOODS_PRICE NULL COUNT:" ,previous_application['AMT_GOODS_PRICE'].isnull().sum())

AMT_GOODS_PRICE NULL COUNT: 385515


previous_application['AMT_GOODS_PRICE'].describe()

count    1.284699e+06
mean     2.278473e+05
std      3.153966e+05
min      0.000000e+00
25%      5.084100e+04
50%      1.123200e+05
75%      2.340000e+05
max      6.905160e+06
Name: AMT_GOODS_PRICE, dtype: float64


sns.set_style('whitegrid') 
sns.distplot(previous_application['AMT_GOODS_PRICE']) 
plt.show()


print("NAME_TYPE_SUITE NULL COUNT:" ,previous_application['NAME_TYPE_SUITE'].isnull().sum())

NAME_TYPE_SUITE NULL COUNT: 820405


previous_application['NAME_TYPE_SUITE'].value_counts()

NAME_TYPE_SUITE
Unaccompanied      508970
Family             213263
Spouse, partner     67069
Children            31566
Other_B             17624
Other_A              9077
Group of people      2240
Name: count, dtype: int64


print("CNT_PAYMENT NULL COUNT:" ,previous_application['CNT_PAYMENT'].isnull().sum())

CNT_PAYMENT NULL COUNT: 372230


previous_application['CNT_PAYMENT'].describe()

count    1.297984e+06
mean     1.605408e+01
std      1.456729e+01
min      0.000000e+00
25%      6.000000e+00
50%      1.200000e+01
75%      2.400000e+01
max      8.400000e+01
Name: CNT_PAYMENT, dtype: float64


sns.set_style('whitegrid') 
sns.boxplot(previous_application['CNT_PAYMENT']) 
plt.show()


previous_application.isnull().sum()

SK_ID_PREV                          0
SK_ID_CURR                          0
NAME_CONTRACT_TYPE                  0
AMT_ANNUITY                    372235
AMT_APPLICATION                     0
AMT_CREDIT                          1
AMT_GOODS_PRICE                385515
WEEKDAY_APPR_PROCESS_START          0
HOUR_APPR_PROCESS_START             0
FLAG_LAST_APPL_PER_CONTRACT         0
NFLAG_LAST_APPL_IN_DAY              0
NAME_CASH_LOAN_PURPOSE              0
NAME_CONTRACT_STATUS                0
DAYS_DECISION                       0
NAME_PAYMENT_TYPE                   0
CODE_REJECT_REASON                  0
NAME_TYPE_SUITE                820405
NAME_CLIENT_TYPE                    0
NAME_GOODS_CATEGORY                 0
NAME_PORTFOLIO                      0
NAME_PRODUCT_TYPE                   0
CHANNEL_TYPE                        0
SELLERPLACE_AREA                    0
NAME_SELLER_INDUSTRY                0
CNT_PAYMENT                    372230
NAME_YIELD_GROUP                    0
PRODUCT_COMBINATION               346
DAYS_FIRST_DRAWING             673065
DAYS_FIRST_DUE                 673065
DAYS_LAST_DUE_1ST_VERSION      673065
DAYS_LAST_DUE                  673065
DAYS_TERMINATION               673065
NFLAG_INSURED_ON_APPROVAL      673065
dtype: int64


print("AMT_CREDIT :" ,previous_application['AMT_CREDIT'].isnull().sum())

AMT_CREDIT : 1


previous_application['AMT_CREDIT'].describe()

count    1.670213e+06
mean     1.961140e+05
std      3.185746e+05
min      0.000000e+00
25%      2.416050e+04
50%      8.054100e+04
75%      2.164185e+05
max      6.905160e+06
Name: AMT_CREDIT, dtype: float64


sns.set_style('whitegrid') 
sns.boxplot(previous_application['AMT_CREDIT']) 
plt.show()


previous_application['PRODUCT_COMBINATION'].value_counts()

PRODUCT_COMBINATION
Cash                              285990
POS household with interest       263622
POS mobile with interest          220670
Cash X-Sell: middle               143883
Cash X-Sell: low                  130248
Card Street                       112582
POS industry with interest         98833
POS household without interest     82908
Card X-Sell                        80582
Cash Street: high                  59639
Cash X-Sell: high                  59301
Cash Street: middle                34658
Cash Street: low                   33834
POS mobile without interest        24082
POS other with interest            23879
POS industry without interest      12602
POS others without interest         2555
Name: count, dtype: int64


class color:
   PURPLE = '\033[95m'
   CYAN = '\033[96m'
   DARKCYAN = '\033[36m'
   BLUE = '\033[94m'
   GREEN = '\033[92m'
   YELLOW = '\033[93m'
   RED = '\033[91m'
   BOLD = '\033[1m'
   UNDERLINE = '\033[4m'
   END = '\033[0m'


obj_dtypes = [i for i in previous_application.select_dtypes(include=object).columns if i not in ["type"]]
num_dtypes = [i for i in previous_application.select_dtypes(include=np.number).columns if i not in ['SK_ID_CURR', 'TARGET']]


print(color.BOLD + color.PURPLE + 'Categorical Columns' + color.END, "\n")
for x in range(len(obj_dtypes)): 
    print(obj_dtypes[x])

Categorical Columns 

NAME_CONTRACT_TYPE
WEEKDAY_APPR_PROCESS_START
FLAG_LAST_APPL_PER_CONTRACT
NAME_CASH_LOAN_PURPOSE
NAME_CONTRACT_STATUS
NAME_PAYMENT_TYPE
CODE_REJECT_REASON
NAME_TYPE_SUITE
NAME_CLIENT_TYPE
NAME_GOODS_CATEGORY
NAME_PORTFOLIO
NAME_PRODUCT_TYPE
CHANNEL_TYPE
NAME_SELLER_INDUSTRY
NAME_YIELD_GROUP
PRODUCT_COMBINATION


print(color.BOLD + color.PURPLE + 'Numerical' + color.END, "\n")
for x in range(len(obj_dtypes)): 
    print(obj_dtypes[x])

Numerical 

NAME_CONTRACT_TYPE
WEEKDAY_APPR_PROCESS_START
FLAG_LAST_APPL_PER_CONTRACT
NAME_CASH_LOAN_PURPOSE
NAME_CONTRACT_STATUS
NAME_PAYMENT_TYPE
CODE_REJECT_REASON
NAME_TYPE_SUITE
NAME_CLIENT_TYPE
NAME_GOODS_CATEGORY
NAME_PORTFOLIO
NAME_PRODUCT_TYPE
CHANNEL_TYPE
NAME_SELLER_INDUSTRY
NAME_YIELD_GROUP
PRODUCT_COMBINATION


app.isnull().sum()

SK_ID_CURR                           0
TARGET                               0
NAME_CONTRACT_TYPE                   0
CODE_GENDER                          0
FLAG_OWN_CAR                         0
FLAG_OWN_REALTY                      0
CNT_CHILDREN                         0
AMT_INCOME_TOTAL                     0
AMT_CREDIT                           0
AMT_ANNUITY                         12
AMT_GOODS_PRICE                    278
NAME_TYPE_SUITE                   1292
NAME_INCOME_TYPE                     0
NAME_EDUCATION_TYPE                  0
NAME_FAMILY_STATUS                   0
NAME_HOUSING_TYPE                    0
REGION_POPULATION_RELATIVE           0
DAYS_BIRTH                           0
DAYS_EMPLOYED                        0
DAYS_REGISTRATION                    0
DAYS_ID_PUBLISH                      0
OWN_CAR_AGE                     202929
FLAG_MOBIL                           0
FLAG_EMP_PHONE                       0
FLAG_WORK_PHONE                      0
FLAG_CONT_MOBILE                     0
FLAG_PHONE                           0
FLAG_EMAIL                           0
OCCUPATION_TYPE                  96391
CNT_FAM_MEMBERS                      2
REGION_RATING_CLIENT                 0
REGION_RATING_CLIENT_W_CITY          0
WEEKDAY_APPR_PROCESS_START           0
HOUR_APPR_PROCESS_START              0
REG_REGION_NOT_LIVE_REGION           0
REG_REGION_NOT_WORK_REGION           0
LIVE_REGION_NOT_WORK_REGION          0
REG_CITY_NOT_LIVE_CITY               0
REG_CITY_NOT_WORK_CITY               0
LIVE_CITY_NOT_WORK_CITY              0
ORGANIZATION_TYPE                    0
EXT_SOURCE_1                    173378
EXT_SOURCE_2                       660
EXT_SOURCE_3                     60965
APARTMENTS_AVG                  156061
BASEMENTAREA_AVG                179943
YEARS_BEGINEXPLUATATION_AVG     150007
YEARS_BUILD_AVG                 204488
COMMONAREA_AVG                  214865
ELEVATORS_AVG                   163891
ENTRANCES_AVG                   154828
FLOORSMAX_AVG                   153020
FLOORSMIN_AVG                   208642
LANDAREA_AVG                    182590
LIVINGAPARTMENTS_AVG            210199
LIVINGAREA_AVG                  154350
NONLIVINGAPARTMENTS_AVG         213514
NONLIVINGAREA_AVG               169682
APARTMENTS_MODE                 156061
BASEMENTAREA_MODE               179943
YEARS_BEGINEXPLUATATION_MODE    150007
YEARS_BUILD_MODE                204488
COMMONAREA_MODE                 214865
ELEVATORS_MODE                  163891
ENTRANCES_MODE                  154828
FLOORSMAX_MODE                  153020
FLOORSMIN_MODE                  208642
LANDAREA_MODE                   182590
LIVINGAPARTMENTS_MODE           210199
LIVINGAREA_MODE                 154350
NONLIVINGAPARTMENTS_MODE        213514
NONLIVINGAREA_MODE              169682
APARTMENTS_MEDI                 156061
BASEMENTAREA_MEDI               179943
YEARS_BEGINEXPLUATATION_MEDI    150007
YEARS_BUILD_MEDI                204488
COMMONAREA_MEDI                 214865
ELEVATORS_MEDI                  163891
ENTRANCES_MEDI                  154828
FLOORSMAX_MEDI                  153020
FLOORSMIN_MEDI                  208642
LANDAREA_MEDI                   182590
LIVINGAPARTMENTS_MEDI           210199
LIVINGAREA_MEDI                 154350
NONLIVINGAPARTMENTS_MEDI        213514
NONLIVINGAREA_MEDI              169682
FONDKAPREMONT_MODE              210295
HOUSETYPE_MODE                  154297
TOTALAREA_MODE                  148431
WALLSMATERIAL_MODE              156341
EMERGENCYSTATE_MODE             145755
OBS_30_CNT_SOCIAL_CIRCLE          1021
DEF_30_CNT_SOCIAL_CIRCLE          1021
OBS_60_CNT_SOCIAL_CIRCLE          1021
DEF_60_CNT_SOCIAL_CIRCLE          1021
DAYS_LAST_PHONE_CHANGE               1
FLAG_DOCUMENT_2                      0
FLAG_DOCUMENT_3                      0
FLAG_DOCUMENT_4                      0
FLAG_DOCUMENT_5                      0
FLAG_DOCUMENT_6                      0
FLAG_DOCUMENT_7                      0
FLAG_DOCUMENT_8                      0
FLAG_DOCUMENT_9                      0
FLAG_DOCUMENT_10                     0
FLAG_DOCUMENT_11                     0
FLAG_DOCUMENT_12                     0
FLAG_DOCUMENT_13                     0
FLAG_DOCUMENT_14                     0
FLAG_DOCUMENT_15                     0
FLAG_DOCUMENT_16                     0
FLAG_DOCUMENT_17                     0
FLAG_DOCUMENT_18                     0
FLAG_DOCUMENT_19                     0
FLAG_DOCUMENT_20                     0
FLAG_DOCUMENT_21                     0
AMT_REQ_CREDIT_BUREAU_HOUR       41519
AMT_REQ_CREDIT_BUREAU_DAY        41519
AMT_REQ_CREDIT_BUREAU_WEEK       41519
AMT_REQ_CREDIT_BUREAU_MON        41519
AMT_REQ_CREDIT_BUREAU_QRT        41519
AMT_REQ_CREDIT_BUREAU_YEAR       41519
dtype: int64


fig = plt.figure(figsize=(18, 6))
miss_application_data = pd.DataFrame((app.isnull().sum()) * 100 / app.shape[0]).reset_index()
miss_application_data.columns = ["index", "percentage"]  # Rename columns for clarity
miss_application_data["type"] = "application_data"
ax = sns.pointplot(x="index", y="percentage", data=miss_application_data, hue="type")
plt.xticks(rotation=90, fontsize=7)
plt.title("Percentage of Missing Values in application_data")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
plt.show()


round(100*(app.isnull().sum()/len(app.index)),2)

SK_ID_CURR                       0.00
TARGET                           0.00
NAME_CONTRACT_TYPE               0.00
CODE_GENDER                      0.00
FLAG_OWN_CAR                     0.00
FLAG_OWN_REALTY                  0.00
CNT_CHILDREN                     0.00
AMT_INCOME_TOTAL                 0.00
AMT_CREDIT                       0.00
AMT_ANNUITY                      0.00
AMT_GOODS_PRICE                  0.09
NAME_TYPE_SUITE                  0.42
NAME_INCOME_TYPE                 0.00
NAME_EDUCATION_TYPE              0.00
NAME_FAMILY_STATUS               0.00
NAME_HOUSING_TYPE                0.00
REGION_POPULATION_RELATIVE       0.00
DAYS_BIRTH                       0.00
DAYS_EMPLOYED                    0.00
DAYS_REGISTRATION                0.00
DAYS_ID_PUBLISH                  0.00
OWN_CAR_AGE                     65.99
FLAG_MOBIL                       0.00
FLAG_EMP_PHONE                   0.00
FLAG_WORK_PHONE                  0.00
FLAG_CONT_MOBILE                 0.00
FLAG_PHONE                       0.00
FLAG_EMAIL                       0.00
OCCUPATION_TYPE                 31.35
CNT_FAM_MEMBERS                  0.00
REGION_RATING_CLIENT             0.00
REGION_RATING_CLIENT_W_CITY      0.00
WEEKDAY_APPR_PROCESS_START       0.00
HOUR_APPR_PROCESS_START          0.00
REG_REGION_NOT_LIVE_REGION       0.00
REG_REGION_NOT_WORK_REGION       0.00
LIVE_REGION_NOT_WORK_REGION      0.00
REG_CITY_NOT_LIVE_CITY           0.00
REG_CITY_NOT_WORK_CITY           0.00
LIVE_CITY_NOT_WORK_CITY          0.00
ORGANIZATION_TYPE                0.00
EXT_SOURCE_1                    56.38
EXT_SOURCE_2                     0.21
EXT_SOURCE_3                    19.83
APARTMENTS_AVG                  50.75
BASEMENTAREA_AVG                58.52
YEARS_BEGINEXPLUATATION_AVG     48.78
YEARS_BUILD_AVG                 66.50
COMMONAREA_AVG                  69.87
ELEVATORS_AVG                   53.30
ENTRANCES_AVG                   50.35
FLOORSMAX_AVG                   49.76
FLOORSMIN_AVG                   67.85
LANDAREA_AVG                    59.38
LIVINGAPARTMENTS_AVG            68.35
LIVINGAREA_AVG                  50.19
NONLIVINGAPARTMENTS_AVG         69.43
NONLIVINGAREA_AVG               55.18
APARTMENTS_MODE                 50.75
BASEMENTAREA_MODE               58.52
YEARS_BEGINEXPLUATATION_MODE    48.78
YEARS_BUILD_MODE                66.50
COMMONAREA_MODE                 69.87
ELEVATORS_MODE                  53.30
ENTRANCES_MODE                  50.35
FLOORSMAX_MODE                  49.76
FLOORSMIN_MODE                  67.85
LANDAREA_MODE                   59.38
LIVINGAPARTMENTS_MODE           68.35
LIVINGAREA_MODE                 50.19
NONLIVINGAPARTMENTS_MODE        69.43
NONLIVINGAREA_MODE              55.18
APARTMENTS_MEDI                 50.75
BASEMENTAREA_MEDI               58.52
YEARS_BEGINEXPLUATATION_MEDI    48.78
YEARS_BUILD_MEDI                66.50
COMMONAREA_MEDI                 69.87
ELEVATORS_MEDI                  53.30
ENTRANCES_MEDI                  50.35
FLOORSMAX_MEDI                  49.76
FLOORSMIN_MEDI                  67.85
LANDAREA_MEDI                   59.38
LIVINGAPARTMENTS_MEDI           68.35
LIVINGAREA_MEDI                 50.19
NONLIVINGAPARTMENTS_MEDI        69.43
NONLIVINGAREA_MEDI              55.18
FONDKAPREMONT_MODE              68.39
HOUSETYPE_MODE                  50.18
TOTALAREA_MODE                  48.27
WALLSMATERIAL_MODE              50.84
EMERGENCYSTATE_MODE             47.40
OBS_30_CNT_SOCIAL_CIRCLE         0.33
DEF_30_CNT_SOCIAL_CIRCLE         0.33
OBS_60_CNT_SOCIAL_CIRCLE         0.33
DEF_60_CNT_SOCIAL_CIRCLE         0.33
DAYS_LAST_PHONE_CHANGE           0.00
FLAG_DOCUMENT_2                  0.00
FLAG_DOCUMENT_3                  0.00
FLAG_DOCUMENT_4                  0.00
FLAG_DOCUMENT_5                  0.00
FLAG_DOCUMENT_6                  0.00
FLAG_DOCUMENT_7                  0.00
FLAG_DOCUMENT_8                  0.00
FLAG_DOCUMENT_9                  0.00
FLAG_DOCUMENT_10                 0.00
FLAG_DOCUMENT_11                 0.00
FLAG_DOCUMENT_12                 0.00
FLAG_DOCUMENT_13                 0.00
FLAG_DOCUMENT_14                 0.00
FLAG_DOCUMENT_15                 0.00
FLAG_DOCUMENT_16                 0.00
FLAG_DOCUMENT_17                 0.00
FLAG_DOCUMENT_18                 0.00
FLAG_DOCUMENT_19                 0.00
FLAG_DOCUMENT_20                 0.00
FLAG_DOCUMENT_21                 0.00
AMT_REQ_CREDIT_BUREAU_HOUR      13.50
AMT_REQ_CREDIT_BUREAU_DAY       13.50
AMT_REQ_CREDIT_BUREAU_WEEK      13.50
AMT_REQ_CREDIT_BUREAU_MON       13.50
AMT_REQ_CREDIT_BUREAU_QRT       13.50
AMT_REQ_CREDIT_BUREAU_YEAR      13.50
dtype: float64


application_data=app.drop([ 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3',
       'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG',
       'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG',
       'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG',
       'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG',
       'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE',
       'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE',
       'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE',
       'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE',
       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI',
       'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI',
       'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI',
       'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI',
       'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI',
       'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'TOTALAREA_MODE',
       'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE',"OWN_CAR_AGE","OCCUPATION_TYPE"],axis=1)


import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

fig = plt.figure(figsize=(18, 6))

# Calculate the percentage of missing values
miss_application_data = pd.DataFrame((application_data.isnull().sum()) * 100 / application_data.shape[0]).reset_index()
miss_application_data.columns = ["index", "percentage"]  # Rename columns for clarity
miss_application_data["type"] = "application_data"

# Correctly specify x and y arguments
ax = sns.pointplot(x="index", y="percentage", data=miss_application_data, hue="type")

plt.xticks(rotation=90, fontsize=7)
plt.title("Percentage of Missing Values in application_data")
plt.ylabel("PERCENTAGE")
plt.xlabel("COLUMNS")

# Set background colors
ax.set_facecolor("k")
fig.set_facecolor("lightgrey")
plt.show()


round(100*(application_data.isnull().sum()/len(application_data.index)),2)

SK_ID_CURR                      0.00
TARGET                          0.00
NAME_CONTRACT_TYPE              0.00
CODE_GENDER                     0.00
FLAG_OWN_CAR                    0.00
FLAG_OWN_REALTY                 0.00
CNT_CHILDREN                    0.00
AMT_INCOME_TOTAL                0.00
AMT_CREDIT                      0.00
AMT_ANNUITY                     0.00
AMT_GOODS_PRICE                 0.09
NAME_TYPE_SUITE                 0.42
NAME_INCOME_TYPE                0.00
NAME_EDUCATION_TYPE             0.00
NAME_FAMILY_STATUS              0.00
NAME_HOUSING_TYPE               0.00
REGION_POPULATION_RELATIVE      0.00
DAYS_BIRTH                      0.00
DAYS_EMPLOYED                   0.00
DAYS_REGISTRATION               0.00
DAYS_ID_PUBLISH                 0.00
FLAG_MOBIL                      0.00
FLAG_EMP_PHONE                  0.00
FLAG_WORK_PHONE                 0.00
FLAG_CONT_MOBILE                0.00
FLAG_PHONE                      0.00
FLAG_EMAIL                      0.00
CNT_FAM_MEMBERS                 0.00
REGION_RATING_CLIENT            0.00
REGION_RATING_CLIENT_W_CITY     0.00
WEEKDAY_APPR_PROCESS_START      0.00
HOUR_APPR_PROCESS_START         0.00
REG_REGION_NOT_LIVE_REGION      0.00
REG_REGION_NOT_WORK_REGION      0.00
LIVE_REGION_NOT_WORK_REGION     0.00
REG_CITY_NOT_LIVE_CITY          0.00
REG_CITY_NOT_WORK_CITY          0.00
LIVE_CITY_NOT_WORK_CITY         0.00
ORGANIZATION_TYPE               0.00
OBS_30_CNT_SOCIAL_CIRCLE        0.33
DEF_30_CNT_SOCIAL_CIRCLE        0.33
OBS_60_CNT_SOCIAL_CIRCLE        0.33
DEF_60_CNT_SOCIAL_CIRCLE        0.33
DAYS_LAST_PHONE_CHANGE          0.00
FLAG_DOCUMENT_2                 0.00
FLAG_DOCUMENT_3                 0.00
FLAG_DOCUMENT_4                 0.00
FLAG_DOCUMENT_5                 0.00
FLAG_DOCUMENT_6                 0.00
FLAG_DOCUMENT_7                 0.00
FLAG_DOCUMENT_8                 0.00
FLAG_DOCUMENT_9                 0.00
FLAG_DOCUMENT_10                0.00
FLAG_DOCUMENT_11                0.00
FLAG_DOCUMENT_12                0.00
FLAG_DOCUMENT_13                0.00
FLAG_DOCUMENT_14                0.00
FLAG_DOCUMENT_15                0.00
FLAG_DOCUMENT_16                0.00
FLAG_DOCUMENT_17                0.00
FLAG_DOCUMENT_18                0.00
FLAG_DOCUMENT_19                0.00
FLAG_DOCUMENT_20                0.00
FLAG_DOCUMENT_21                0.00
AMT_REQ_CREDIT_BUREAU_HOUR     13.50
AMT_REQ_CREDIT_BUREAU_DAY      13.50
AMT_REQ_CREDIT_BUREAU_WEEK     13.50
AMT_REQ_CREDIT_BUREAU_MON      13.50
AMT_REQ_CREDIT_BUREAU_QRT      13.50
AMT_REQ_CREDIT_BUREAU_YEAR     13.50
dtype: float64


print("AMT_REQ_CREDIT_BUREAU_DAY NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_DAY'].isnull().sum())

AMT_REQ_CREDIT_BUREAU_DAY NAN COUNT : 41519


application_data['AMT_REQ_CREDIT_BUREAU_DAY'].describe()

count    265992.000000
mean          0.007000
std           0.110757
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           9.000000
Name: AMT_REQ_CREDIT_BUREAU_DAY, dtype: float64


print("AMT_REQ_CREDIT_BUREAU_HOUR NAN COUNT :" ,application_data['AMT_REQ_CREDIT_BUREAU_HOUR'].isnull().sum())

AMT_REQ_CREDIT_BUREAU_HOUR NAN COUNT : 41519


application_data['AMT_REQ_CREDIT_BUREAU_HOUR'].describe()

count    265992.000000
mean          0.006402
std           0.083849
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           4.000000
Name: AMT_REQ_CREDIT_BUREAU_HOUR, dtype: float64


application_data.isnull().sum()

SK_ID_CURR                         0
TARGET                             0
NAME_CONTRACT_TYPE                 0
CODE_GENDER                        0
FLAG_OWN_CAR                       0
FLAG_OWN_REALTY                    0
CNT_CHILDREN                       0
AMT_INCOME_TOTAL                   0
AMT_CREDIT                         0
AMT_ANNUITY                       12
AMT_GOODS_PRICE                  278
NAME_TYPE_SUITE                 1292
NAME_INCOME_TYPE                   0
NAME_EDUCATION_TYPE                0
NAME_FAMILY_STATUS                 0
NAME_HOUSING_TYPE                  0
REGION_POPULATION_RELATIVE         0
DAYS_BIRTH                         0
DAYS_EMPLOYED                      0
DAYS_REGISTRATION                  0
DAYS_ID_PUBLISH                    0
FLAG_MOBIL                         0
FLAG_EMP_PHONE                     0
FLAG_WORK_PHONE                    0
FLAG_CONT_MOBILE                   0
FLAG_PHONE                         0
FLAG_EMAIL                         0
CNT_FAM_MEMBERS                    2
REGION_RATING_CLIENT               0
REGION_RATING_CLIENT_W_CITY        0
WEEKDAY_APPR_PROCESS_START         0
HOUR_APPR_PROCESS_START            0
REG_REGION_NOT_LIVE_REGION         0
REG_REGION_NOT_WORK_REGION         0
LIVE_REGION_NOT_WORK_REGION        0
REG_CITY_NOT_LIVE_CITY             0
REG_CITY_NOT_WORK_CITY             0
LIVE_CITY_NOT_WORK_CITY            0
ORGANIZATION_TYPE                  0
OBS_30_CNT_SOCIAL_CIRCLE        1021
DEF_30_CNT_SOCIAL_CIRCLE        1021
OBS_60_CNT_SOCIAL_CIRCLE        1021
DEF_60_CNT_SOCIAL_CIRCLE        1021
DAYS_LAST_PHONE_CHANGE             1
FLAG_DOCUMENT_2                    0
FLAG_DOCUMENT_3                    0
FLAG_DOCUMENT_4                    0
FLAG_DOCUMENT_5                    0
FLAG_DOCUMENT_6                    0
FLAG_DOCUMENT_7                    0
FLAG_DOCUMENT_8                    0
FLAG_DOCUMENT_9                    0
FLAG_DOCUMENT_10                   0
FLAG_DOCUMENT_11                   0
FLAG_DOCUMENT_12                   0
FLAG_DOCUMENT_13                   0
FLAG_DOCUMENT_14                   0
FLAG_DOCUMENT_15                   0
FLAG_DOCUMENT_16                   0
FLAG_DOCUMENT_17                   0
FLAG_DOCUMENT_18                   0
FLAG_DOCUMENT_19                   0
FLAG_DOCUMENT_20                   0
FLAG_DOCUMENT_21                   0
AMT_REQ_CREDIT_BUREAU_HOUR     41519
AMT_REQ_CREDIT_BUREAU_DAY      41519
AMT_REQ_CREDIT_BUREAU_WEEK     41519
AMT_REQ_CREDIT_BUREAU_MON      41519
AMT_REQ_CREDIT_BUREAU_QRT      41519
AMT_REQ_CREDIT_BUREAU_YEAR     41519
dtype: int64


print("AMT_ANNUITY  :" ,application_data['AMT_ANNUITY'].isnull().sum())

AMT_ANNUITY  : 12


application_data['AMT_ANNUITY'].describe()

count    307499.000000
mean      27108.573909
std       14493.737315
min        1615.500000
25%       16524.000000
50%       24903.000000
75%       34596.000000
max      258025.500000
Name: AMT_ANNUITY, dtype: float64


sns.set_style('whitegrid') 
sns.distplot(application_data['AMT_ANNUITY']) 
plt.show()


print("AMT_GOODS_PRICE   :" ,application_data['AMT_GOODS_PRICE'].isnull().sum())

AMT_GOODS_PRICE   : 278


application_data['AMT_GOODS_PRICE'].describe()

count    3.072330e+05
mean     5.383962e+05
std      3.694465e+05
min      4.050000e+04
25%      2.385000e+05
50%      4.500000e+05
75%      6.795000e+05
max      4.050000e+06
Name: AMT_GOODS_PRICE, dtype: float64


sns.set_style('whitegrid') 
sns.distplot(application_data['AMT_GOODS_PRICE']) 
plt.show()


import statistics 
statistics.mode(application_data['DAYS_LAST_PHONE_CHANGE'])

0.0


print(type(application_data.info()))

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Data columns (total 70 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   SK_ID_CURR                   307511 non-null  int64  
 1   TARGET                       307511 non-null  int64  
 2   NAME_CONTRACT_TYPE           307511 non-null  object 
 3   CODE_GENDER                  307511 non-null  object 
 4   FLAG_OWN_CAR                 307511 non-null  object 
 5   FLAG_OWN_REALTY              307511 non-null  object 
 6   CNT_CHILDREN                 307511 non-null  int64  
 7   AMT_INCOME_TOTAL             307511 non-null  float64
 8   AMT_CREDIT                   307511 non-null  float64
 9   AMT_ANNUITY                  307499 non-null  float64
 10  AMT_GOODS_PRICE              307233 non-null  float64
 11  NAME_TYPE_SUITE              306219 non-null  object 
 12  NAME_INCOME_TYPE             307511 non-null  object 
 13  NAME_EDUCATION_TYPE          307511 non-null  object 
 14  NAME_FAMILY_STATUS           307511 non-null  object 
 15  NAME_HOUSING_TYPE            307511 non-null  object 
 16  REGION_POPULATION_RELATIVE   307511 non-null  float64
 17  DAYS_BIRTH                   307511 non-null  int64  
 18  DAYS_EMPLOYED                307511 non-null  int64  
 19  DAYS_REGISTRATION            307511 non-null  float64
 20  DAYS_ID_PUBLISH              307511 non-null  int64  
 21  FLAG_MOBIL                   307511 non-null  int64  
 22  FLAG_EMP_PHONE               307511 non-null  int64  
 23  FLAG_WORK_PHONE              307511 non-null  int64  
 24  FLAG_CONT_MOBILE             307511 non-null  int64  
 25  FLAG_PHONE                   307511 non-null  int64  
 26  FLAG_EMAIL                   307511 non-null  int64  
 27  CNT_FAM_MEMBERS              307509 non-null  float64
 28  REGION_RATING_CLIENT         307511 non-null  int64  
 29  REGION_RATING_CLIENT_W_CITY  307511 non-null  int64  
 30  WEEKDAY_APPR_PROCESS_START   307511 non-null  object 
 31  HOUR_APPR_PROCESS_START      307511 non-null  int64  
 32  REG_REGION_NOT_LIVE_REGION   307511 non-null  int64  
 33  REG_REGION_NOT_WORK_REGION   307511 non-null  int64  
 34  LIVE_REGION_NOT_WORK_REGION  307511 non-null  int64  
 35  REG_CITY_NOT_LIVE_CITY       307511 non-null  int64  
 36  REG_CITY_NOT_WORK_CITY       307511 non-null  int64  
 37  LIVE_CITY_NOT_WORK_CITY      307511 non-null  int64  
 38  ORGANIZATION_TYPE            307511 non-null  object 
 39  OBS_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 40  DEF_30_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 41  OBS_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 42  DEF_60_CNT_SOCIAL_CIRCLE     306490 non-null  float64
 43  DAYS_LAST_PHONE_CHANGE       307510 non-null  float64
 44  FLAG_DOCUMENT_2              307511 non-null  int64  
 45  FLAG_DOCUMENT_3              307511 non-null  int64  
 46  FLAG_DOCUMENT_4              307511 non-null  int64  
 47  FLAG_DOCUMENT_5              307511 non-null  int64  
 48  FLAG_DOCUMENT_6              307511 non-null  int64  
 49  FLAG_DOCUMENT_7              307511 non-null  int64  
 50  FLAG_DOCUMENT_8              307511 non-null  int64  
 51  FLAG_DOCUMENT_9              307511 non-null  int64  
 52  FLAG_DOCUMENT_10             307511 non-null  int64  
 53  FLAG_DOCUMENT_11             307511 non-null  int64  
 54  FLAG_DOCUMENT_12             307511 non-null  int64  
 55  FLAG_DOCUMENT_13             307511 non-null  int64  
 56  FLAG_DOCUMENT_14             307511 non-null  int64  
 57  FLAG_DOCUMENT_15             307511 non-null  int64  
 58  FLAG_DOCUMENT_16             307511 non-null  int64  
 59  FLAG_DOCUMENT_17             307511 non-null  int64  
 60  FLAG_DOCUMENT_18             307511 non-null  int64  
 61  FLAG_DOCUMENT_19             307511 non-null  int64  
 62  FLAG_DOCUMENT_20             307511 non-null  int64  
 63  FLAG_DOCUMENT_21             307511 non-null  int64  
 64  AMT_REQ_CREDIT_BUREAU_HOUR   265992 non-null  float64
 65  AMT_REQ_CREDIT_BUREAU_DAY    265992 non-null  float64
 66  AMT_REQ_CREDIT_BUREAU_WEEK   265992 non-null  float64
 67  AMT_REQ_CREDIT_BUREAU_MON    265992 non-null  float64
 68  AMT_REQ_CREDIT_BUREAU_QRT    265992 non-null  float64
 69  AMT_REQ_CREDIT_BUREAU_YEAR   265992 non-null  float64
dtypes: float64(18), int64(41), object(11)
memory usage: 164.2+ MB
<class 'NoneType'>


application_data['DAYS_BIRTH'] = abs(application_data['DAYS_BIRTH'])
application_data['DAYS_ID_PUBLISH'] = abs(application_data['DAYS_ID_PUBLISH'])
application_data['DAYS_ID_PUBLISH'] = abs(application_data['DAYS_ID_PUBLISH'])
application_data['DAYS_LAST_PHONE_CHANGE'] = abs(application_data['DAYS_LAST_PHONE_CHANGE'])


display("application_data")
display(application_data.head())

'application_data'


obj_dtypes = [i for i in application_data.select_dtypes(include=object).columns if i not in ["type"]]
num_dtypes = [i for i in application_data.select_dtypes(include=np.number).columns if i not in ['SK_ID_CURR', 'TARGET']]


print(color.BOLD + color.PURPLE + 'Categorical Columns' + color.END, "\n")
for x in range(len(obj_dtypes)): 
    print(obj_dtypes[x])

Categorical Columns 

NAME_CONTRACT_TYPE
CODE_GENDER
FLAG_OWN_CAR
FLAG_OWN_REALTY
NAME_TYPE_SUITE
NAME_INCOME_TYPE
NAME_EDUCATION_TYPE
NAME_FAMILY_STATUS
NAME_HOUSING_TYPE
WEEKDAY_APPR_PROCESS_START
ORGANIZATION_TYPE


print(color.BOLD + color.PURPLE +"Numerical Columns" + color.END, "\n")
for x in range(len(num_dtypes)): 
    print(num_dtypes[x])

Numerical Columns 

CNT_CHILDREN
AMT_INCOME_TOTAL
AMT_CREDIT
AMT_ANNUITY
AMT_GOODS_PRICE
REGION_POPULATION_RELATIVE
DAYS_BIRTH
DAYS_EMPLOYED
DAYS_REGISTRATION
DAYS_ID_PUBLISH
FLAG_MOBIL
FLAG_EMP_PHONE
FLAG_WORK_PHONE
FLAG_CONT_MOBILE
FLAG_PHONE
FLAG_EMAIL
CNT_FAM_MEMBERS
REGION_RATING_CLIENT
REGION_RATING_CLIENT_W_CITY
HOUR_APPR_PROCESS_START
REG_REGION_NOT_LIVE_REGION
REG_REGION_NOT_WORK_REGION
LIVE_REGION_NOT_WORK_REGION
REG_CITY_NOT_LIVE_CITY
REG_CITY_NOT_WORK_CITY
LIVE_CITY_NOT_WORK_CITY
OBS_30_CNT_SOCIAL_CIRCLE
DEF_30_CNT_SOCIAL_CIRCLE
OBS_60_CNT_SOCIAL_CIRCLE
DEF_60_CNT_SOCIAL_CIRCLE
DAYS_LAST_PHONE_CHANGE
FLAG_DOCUMENT_2
FLAG_DOCUMENT_3
FLAG_DOCUMENT_4
FLAG_DOCUMENT_5
FLAG_DOCUMENT_6
FLAG_DOCUMENT_7
FLAG_DOCUMENT_8
FLAG_DOCUMENT_9
FLAG_DOCUMENT_10
FLAG_DOCUMENT_11
FLAG_DOCUMENT_12
FLAG_DOCUMENT_13
FLAG_DOCUMENT_14
FLAG_DOCUMENT_15
FLAG_DOCUMENT_16
FLAG_DOCUMENT_17
FLAG_DOCUMENT_18
FLAG_DOCUMENT_19
FLAG_DOCUMENT_20
FLAG_DOCUMENT_21
AMT_REQ_CREDIT_BUREAU_HOUR
AMT_REQ_CREDIT_BUREAU_DAY
AMT_REQ_CREDIT_BUREAU_WEEK
AMT_REQ_CREDIT_BUREAU_MON
AMT_REQ_CREDIT_BUREAU_QRT
AMT_REQ_CREDIT_BUREAU_YEAR


fig = plt.figure(figsize=(13,6))
plt.subplot(121)
application_data["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of gender")
plt.show()


plt.figure(figsize=(14,7))
plt.subplot(121)
application_data["TARGET"].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",7),startangle = 60,labels=["repayer","defaulter"],
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.1,0],shadow =True)
plt.title("Distribution of target variable")

plt.subplot(122)
ax = application_data["TARGET"].value_counts().plot(kind="barh")

for i,j in enumerate(application_data["TARGET"].value_counts().values):
    ax.text(.7,i,j,weight = "bold",fontsize=20)

plt.title("Count of target variable")
plt.show()


application_data_x = application_data[[x for x in application_data.columns if x not in ["TARGET"]]]
previous_application_x = previous_application[[x for x in previous_application.columns if x not in ["TARGET"]]]
application_data_x["type"] = "application_data"
previous_application_x["type"] = "previous_application"
data = pd.concat([application_data_x,previous_application_x],axis=0)


plt.figure(figsize=(14,7))
plt.subplot(121)
data[data["type"] == "application_data"]["NAME_CONTRACT_TYPE"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orange","red"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("distribution of contract types in application_data")

plt.subplot(122)
data[data["type"] == "previous_application"]["NAME_CONTRACT_TYPE"].value_counts().plot.pie(autopct = "%1.2f%%",colors = ["red","yellow","green",'BLACK'],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.ylabel("")
plt.title("distribution of contract types in previous_application")
plt.show()

plt.show()


fig = plt.figure(figsize=(13,6))
plt.subplot(121)
data[data["type"] == "application_data"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("distribution of gender in application_data")
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

fig = plt.figure(figsize=(13, 6))
plt.subplot(121)
ax = sns.countplot(x="NAME_CONTRACT_TYPE", hue="CODE_GENDER", data=data[data["type"] == "application_data"], palette=["r", "b", "g"])
ax.set_facecolor("lightgrey")
ax.set_title("Distribution of Contract Type by Gender - application_data")

plt.show()


fig = plt.figure(figsize=(13,6))

plt.subplot(121)
data["FLAG_OWN_CAR"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["gold","orangered"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0],shadow =True)
plt.title("distribution of client owning a car")

plt.subplot(122)
data[data["FLAG_OWN_CAR"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["b","orangered"],startangle = 90,
                                                                         wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("distribution of client owning a car by gender")

plt.show()


plt.figure(figsize=(13,6))
plt.subplot(121)
data["FLAG_OWN_REALTY"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["skyblue","gold"],startangle = 90,
                                              wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[0.05,0],shadow =True)
plt.title("Distribution of client owns a house or flat")

plt.subplot(122)
data[data["FLAG_OWN_REALTY"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orangered","b"],startangle = 90,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of client owning a house or flat by gender")
plt.show()


fig = plt.figure(figsize=(12, 10))

# Plot 1: Distribution of Number of Children by Repayment Status
plt.subplot(211)
sns.countplot(x="CNT_CHILDREN", hue="TARGET", data=application_data, palette="Set1")
plt.legend(loc="upper center")
plt.title("Distribution of Number of Children Client Has by Repayment Status")

# Plot 2: Distribution of Number of Family Members by Repayment Status
plt.subplot(212)
sns.countplot(x="CNT_FAM_MEMBERS", hue="TARGET", data=application_data, palette="Set1")
plt.legend(loc="upper center")
plt.title("Distribution of Number of Family Members Client Has by Repayment Status")

# Set figure background color
fig.set_facecolor("lightblue")

plt.tight_layout()
plt.show()


default = application_data[application_data["TARGET"]==1][[ 'NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]
non_default = application_data[application_data["TARGET"]==0][[ 'NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]

d_cols = ['NAME_CONTRACT_TYPE', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']
d_length = len(d_cols)

fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
    plt.subplot(1,4,j+1)
    default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism"),startangle = 90,
                                        wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
    circ = plt.Circle((0,0),.7,color="white")
    plt.gca().add_artist(circ)
    plt.ylabel("")
    plt.title(i+"-Defaulter")


fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
    plt.subplot(1,4,j+1)
    non_default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",3),startangle = 90,
                                           wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
    circ = plt.Circle((0,0),.7,color="white")
    plt.gca().add_artist(circ)
    plt.ylabel("")
    plt.title(i+"-Repayer")


cols = [ 'AMT_INCOME_TOTAL', 'AMT_CREDIT','AMT_ANNUITY', 'AMT_GOODS_PRICE']
length = len(cols)
cs = ["r","b","g","k"]

ax = plt.figure(figsize=(18,18))
ax.set_facecolor("lightgrey")
for i,j,k in itertools.zip_longest(cols,range(length),cs):
    plt.subplot(2,2,j+1)
    sns.distplot(data[data[i].notnull()][i],color=k)
    plt.axvline(data[i].mean(),label = "mean",linestyle="dashed",color="k")
    plt.legend(loc="best")
    plt.title(i)
    plt.subplots_adjust(hspace = .2)


import matplotlib.pyplot as plt
import seaborn as sns
import itertools

stat = df_new["statistic"].unique().tolist()
length = len(stat)

plt.figure(figsize=(13, 15))

# Loop through each statistic and create a subplot
for i, j in itertools.zip_longest(stat, range(length)):
    plt.subplot(2, 2, j + 1)
    # Use keyword arguments for x, y, and hue
    sns.barplot(x="amount_type", y="amount", hue="type", 
                data=df_new[df_new["statistic"] == i], palette=["g", "r"])
    
    plt.title(i + " -- Defaulters vs Non-defaulters")
    plt.subplots_adjust(hspace=0.4)

# Set the background color of each figure
fig = plt.gcf()
fig.set_facecolor("lightgrey")

plt.show()


import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

# Plotting
plt.figure(figsize=(12, 5))
ax = sns.barplot(x="amt_type", y="amount", data=df_gen, hue="gender", palette="Set1")
plt.title("Average Income, Credit, Annuity & Goods Price by Gender")
plt.xticks(rotation=45)
plt.show()


fig = plt.figure(figsize=(10,8))
plt.scatter(application_data[application_data["TARGET"]==0]['AMT_ANNUITY'],application_data[application_data["TARGET"]==0]['AMT_CREDIT'],s=35,
            color="b",alpha=.5,label="REPAYER",linewidth=.5,edgecolor="k")
plt.scatter(application_data[application_data["TARGET"]==1]['AMT_ANNUITY'],application_data[application_data["TARGET"]==1]['AMT_CREDIT'],s=35,
            color="r",alpha=.2,label="DEFAULTER",linewidth=.5,edgecolor="k")
plt.legend(loc="best",prop={"size":15})
plt.xlabel("AMT_ANNUITY")
plt.ylabel("AMT_CREDIT")
plt.title("Scatter plot between credit amount and annuity amount")
plt.show()


amt = application_data[[ 'AMT_INCOME_TOTAL','AMT_CREDIT',
                         'AMT_ANNUITY', 'AMT_GOODS_PRICE',"TARGET"]]
amt = amt[(amt["AMT_GOODS_PRICE"].notnull()) & (amt["AMT_ANNUITY"].notnull())]
sns.pairplot(amt,hue="TARGET",palette=["b","r"])
plt.show()


plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=data["NAME_TYPE_SUITE"],
              palette="Set2",
              order=data["NAME_TYPE_SUITE"].value_counts().index[:5])
plt.title("Distribution of Suite type")

plt.subplot(122)
sns.countplot(y=data["NAME_TYPE_SUITE"],
              hue=data["CODE_GENDER"],palette="Set2",
              order=data["NAME_TYPE_SUITE"].value_counts().index[:5])
plt.ylabel("")
plt.title("Distribution of Suite type by gender")
plt.subplots_adjust(wspace = .4)


plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=data["NAME_INCOME_TYPE"],
              palette="Set2",
              order=data["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.title("Distribution of client income type")

plt.subplot(122)
sns.countplot(y=data["NAME_INCOME_TYPE"],
              hue=data["CODE_GENDER"],
              palette="Set2",
              order=data["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.ylabel("")
plt.title("Distribution of client income  type by gender")
plt.subplots_adjust(wspace = .4)


cols = ['DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE', 'DAYS_TERMINATION']
plt.figure(figsize=(12,6))
sns.heatmap(previous_application[cols].describe()[1:].transpose(),
            annot=True,linewidth=2,linecolor="k",cmap=sns.color_palette("inferno"))
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

# Select only numeric columns for correlation calculation
numeric_cols = application_data.select_dtypes(include='number')

# Calculate the correlation matrix
corrmat = numeric_cols.corr()

# Plot the heatmap
f, ax = plt.subplots(figsize=(8, 8))
sns.heatmap(corrmat, ax=ax, cmap="rainbow", annot=True, fmt=".2f")
plt.title("Correlation Matrix - Application Data")
plt.show()


import matplotlib.pyplot as plt
import seaborn as sns

# Select only numeric columns for correlation calculation
numeric_cols = previous_application.select_dtypes(include='number')

# Calculate the correlation matrix
corrmat = numeric_cols.corr()

# Plot the heatmap
f, ax = plt.subplots(figsize=(8, 8))
sns.heatmap(corrmat, ax=ax, cmap="rainbow", annot=True, fmt=".2f")
plt.title("Correlation Matrix - Previous Application Data")
plt.show()


import numpy as np

# Select only numeric columns
numeric_cols = previous_application.select_dtypes(include='number')

# Calculate the correlation matrix for numeric columns
corrmat = numeric_cols.corr()

# Create a DataFrame to store the correlation values above the diagonal
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))

# Unstack the correlation matrix and reset the index
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop NaN values (which are not correlations)
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values and convert to absolute values
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort the correlations by their absolute values in descending order
top_correlations = corrdf.sort_values(by='Correlation', ascending=False).head(10)

# Display the top 10 correlations
print(top_correlations)

                          Var1                Var2  Correlation
88             AMT_GOODS_PRICE     AMT_APPLICATION         1.00
89             AMT_GOODS_PRICE          AMT_CREDIT         0.99
71                  AMT_CREDIT     AMT_APPLICATION         0.98
269           DAYS_TERMINATION       DAYS_LAST_DUE         0.93
87             AMT_GOODS_PRICE         AMT_ANNUITY         0.82
70                  AMT_CREDIT         AMT_ANNUITY         0.82
53             AMT_APPLICATION         AMT_ANNUITY         0.81
232  DAYS_LAST_DUE_1ST_VERSION  DAYS_FIRST_DRAWING         0.80
173                CNT_PAYMENT     AMT_APPLICATION         0.68
174                CNT_PAYMENT          AMT_CREDIT         0.67


df_repayer = application_data[application_data['TARGET'] == 0]
df_defaulter = application_data[application_data['TARGET'] == 1]


import numpy as np

# Select only numeric columns from df_repayer
numeric_cols_repayer = df_repayer.select_dtypes(include='number')

# Calculate the correlation matrix for numeric columns
corrmat = numeric_cols_repayer.corr()

# Create a DataFrame to store the correlation values above the diagonal
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))

# Unstack the correlation matrix and reset the index
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop NaN values (which are not correlations)
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values and convert to absolute values
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort the correlations by their absolute values in descending order
top_correlations_repayer = corrdf.sort_values(by='Correlation', ascending=False).head(10)

# Display the top 10 correlations
print(top_correlations_repayer)

                             Var1                        Var2  Correlation
776                FLAG_EMP_PHONE               DAYS_EMPLOYED         1.00
1798     OBS_60_CNT_SOCIAL_CIRCLE    OBS_30_CNT_SOCIAL_CIRCLE         1.00
358               AMT_GOODS_PRICE                  AMT_CREDIT         0.99
1199  REGION_RATING_CLIENT_W_CITY        REGION_RATING_CLIENT         0.95
1064              CNT_FAM_MEMBERS                CNT_CHILDREN         0.88
1858     DEF_60_CNT_SOCIAL_CIRCLE    DEF_30_CNT_SOCIAL_CIRCLE         0.86
1439  LIVE_REGION_NOT_WORK_REGION  REG_REGION_NOT_WORK_REGION         0.86
1619      LIVE_CITY_NOT_WORK_CITY      REG_CITY_NOT_WORK_CITY         0.83
359               AMT_GOODS_PRICE                 AMT_ANNUITY         0.78
299                   AMT_ANNUITY                  AMT_CREDIT         0.77


import numpy as np

# Select only numeric columns from df_defaulter
numeric_cols_defaulter = df_defaulter.select_dtypes(include='number')

# Calculate the correlation matrix for numeric columns
corrmat = numeric_cols_defaulter.corr()

# Create a DataFrame to store the correlation values above the diagonal
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))

# Unstack the correlation matrix and reset the index
corrdf = corrdf.unstack().reset_index()
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop NaN values (which are not correlations)
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values and convert to absolute values
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort the correlations by their absolute values in descending order
top_correlations_defaulter = corrdf.sort_values(by='Correlation', ascending=False).head(10)

# Display the top 10 correlations
print(top_correlations_defaulter)

                             Var1                        Var2  Correlation
1798     OBS_60_CNT_SOCIAL_CIRCLE    OBS_30_CNT_SOCIAL_CIRCLE         1.00
776                FLAG_EMP_PHONE               DAYS_EMPLOYED         1.00
358               AMT_GOODS_PRICE                  AMT_CREDIT         0.98
1199  REGION_RATING_CLIENT_W_CITY        REGION_RATING_CLIENT         0.96
1064              CNT_FAM_MEMBERS                CNT_CHILDREN         0.89
1858     DEF_60_CNT_SOCIAL_CIRCLE    DEF_30_CNT_SOCIAL_CIRCLE         0.87
1439  LIVE_REGION_NOT_WORK_REGION  REG_REGION_NOT_WORK_REGION         0.85
1619      LIVE_CITY_NOT_WORK_CITY      REG_CITY_NOT_WORK_CITY         0.78
299                   AMT_ANNUITY                  AMT_CREDIT         0.75
359               AMT_GOODS_PRICE                 AMT_ANNUITY         0.75


mergeddf =  pd.merge(application_data,previous_application,on='SK_ID_CURR')
mergeddf.head()


y = mergeddf.groupby('SK_ID_CURR').size()
dfA = mergeddf.groupby('SK_ID_CURR').agg({'TARGET': np.sum})
dfA['count'] = y
display(dfA.head(10))


dfA.sort_values(by = 'count',ascending=False).head(10)


df_repayer = dfA[dfA['TARGET'] == 0]
df_defaulter = dfA[dfA['TARGET'] == 1]


df_repayer.sort_values(by = 'count',ascending=False).head(10)


df_defaulter.sort_values(by = 'count',ascending=False).head(10)


application_data=pd.read_csv("application_data.csv")
previous_application=pd.read_csv("previous_application.csv")
columns_description=pd.read_csv("columns_description.csv",encoding='ISO-8859-1')
# The file I'm trying to read might not actually be encoded in utf-8. It could be using another encoding like ISO-8859-1


application_data=application_data.drop([ 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3',
       'APARTMENTS_AVG', 'BASEMENTAREA_AVG', 'YEARS_BEGINEXPLUATATION_AVG',
       'YEARS_BUILD_AVG', 'COMMONAREA_AVG', 'ELEVATORS_AVG', 'ENTRANCES_AVG',
       'FLOORSMAX_AVG', 'FLOORSMIN_AVG', 'LANDAREA_AVG',
       'LIVINGAPARTMENTS_AVG', 'LIVINGAREA_AVG', 'NONLIVINGAPARTMENTS_AVG',
       'NONLIVINGAREA_AVG', 'APARTMENTS_MODE', 'BASEMENTAREA_MODE',
       'YEARS_BEGINEXPLUATATION_MODE', 'YEARS_BUILD_MODE', 'COMMONAREA_MODE',
       'ELEVATORS_MODE', 'ENTRANCES_MODE', 'FLOORSMAX_MODE', 'FLOORSMIN_MODE',
       'LANDAREA_MODE', 'LIVINGAPARTMENTS_MODE', 'LIVINGAREA_MODE',
       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAREA_MODE', 'APARTMENTS_MEDI',
       'BASEMENTAREA_MEDI', 'YEARS_BEGINEXPLUATATION_MEDI', 'YEARS_BUILD_MEDI',
       'COMMONAREA_MEDI', 'ELEVATORS_MEDI', 'ENTRANCES_MEDI', 'FLOORSMAX_MEDI',
       'FLOORSMIN_MEDI', 'LANDAREA_MEDI', 'LIVINGAPARTMENTS_MEDI',
       'LIVINGAREA_MEDI', 'NONLIVINGAPARTMENTS_MEDI', 'NONLIVINGAREA_MEDI',
       'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'TOTALAREA_MODE',
       'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE',"OWN_CAR_AGE","OCCUPATION_TYPE"],axis=1)
previous_application=previous_application.drop([ 'AMT_DOWN_PAYMENT', 'RATE_DOWN_PAYMENT', 'RATE_INTEREST_PRIMARY',
       "RATE_INTEREST_PRIVILEGED"],axis=1)


pd.set_option("display.max_rows", None, "display.max_columns", None)
combined_df= pd.merge(application_data, previous_application, on='SK_ID_CURR', how='inner')

combined_df.sort_values(by=['SK_ID_CURR','SK_ID_PREV'],ascending=[True,True],inplace=True)
display(combined_df.head(10))


combined_df.drop_duplicates(subset='SK_ID_CURR',inplace=True,keep='last')
combined_df.head()


combined_df['DAYS_BIRTH'] = abs(combined_df['DAYS_BIRTH'])
combined_df['DAYS_ID_PUBLISH'] = abs(combined_df['DAYS_ID_PUBLISH'])
combined_df['DAYS_ID_PUBLISH'] = abs(combined_df['DAYS_ID_PUBLISH'])
combined_df['DAYS_LAST_PHONE_CHANGE'] = abs(combined_df['DAYS_LAST_PHONE_CHANGE'])


# Select only numeric columns from the combined_df
numeric_cols_combined = combined_df.select_dtypes(include='number')

# Calculate the correlation matrix for numeric columns
corrmat = numeric_cols_combined.corr()

# Create a heatmap for the correlation matrix
f, ax = plt.subplots(figsize=(8, 8))
sns.heatmap(corrmat, ax=ax, cmap="rainbow")
plt.show()


# Select only the numeric columns in the DataFrame
numeric_cols_combined = combined_df.select_dtypes(include='number')

# Compute the correlation matrix for numeric columns only
corrmat = numeric_cols_combined.corr()

# Apply the upper triangle mask to avoid duplicate correlations
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))


# Unstack the correlation matrix and reset index
corrdf = corrdf.unstack().reset_index()

# Rename columns for clarity
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop rows with NaN values (those from the lower triangle)
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values to two decimal places and take the absolute value
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort by the correlation values in descending order and get the top 10
top_10_corr = corrdf.sort_values(by='Correlation', ascending=False).head(10)

# Display the top 10 correlations
top_10_corr


fig = plt.figure(figsize=(13,6))
plt.subplot(121)
combined_df["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of gender")
plt.show()


plt.figure(figsize=(14,7))
plt.subplot(121)
combined_df["TARGET"].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",7),startangle = 60,labels=["repayer","defaulter"],
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.1,0],shadow =True)
plt.title("Distribution of target variable")

plt.subplot(122)
ax = combined_df["TARGET"].value_counts().plot(kind="barh")

for i,j in enumerate(combined_df["TARGET"].value_counts().values):
    ax.text(.7,i,j,weight = "bold",fontsize=20)

plt.title("Count of target variable")
plt.show()


plt.figure(figsize=(14,7))
plt.subplot(121)
combined_df["NAME_CONTRACT_TYPE_x"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orange","red"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"white"},shadow =True)
circ = plt.Circle((0,0),.7,color="white")
plt.gca().add_artist(circ)
plt.title("distribution of contract types in combined_df")

plt.show()


fig = plt.figure(figsize=(13,6))
plt.subplot(121)
combined_df["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["red","yellow"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of gender")
plt.show()


fig  = plt.figure(figsize=(13,6))
plt.subplot(121)
ax = sns.countplot(x="NAME_CONTRACT_TYPE_x", hue="CODE_GENDER", data=combined_df, palette=["r", "b", "g"])
ax.set_facecolor("lightgrey")
ax.set_title("Distribution of Contract type by gender - application_data")

plt.show()


fig = plt.figure(figsize=(13,6))

plt.subplot(121)
combined_df["FLAG_OWN_CAR"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["gold","orangered"],startangle = 60,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0],shadow =True)
plt.title("distribution of client owning a car")

plt.subplot(122)
combined_df[combined_df["FLAG_OWN_CAR"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["b","orangered"],startangle = 90,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("distribution of client owning a car by gender")

plt.show()


plt.figure(figsize=(13,6))
plt.subplot(121)
combined_df["FLAG_OWN_REALTY"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["skyblue","gold"],startangle = 90,
                                              wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[0.05,0],shadow =True)
plt.title("Distribution of client owns a house or flat")

plt.subplot(122)
combined_df[combined_df["FLAG_OWN_REALTY"] == "Y"]["CODE_GENDER"].value_counts().plot.pie(autopct = "%1.0f%%",colors = ["orangered","b"],startangle = 90,
                                                                        wedgeprops={"linewidth":2,"edgecolor":"k"},explode=[.05,0,0],shadow =True)
plt.title("Distribution of client owning a house or flat by gender")
plt.show()


fig = plt.figure(figsize=(12,10))

# First subplot
plt.subplot(211)
sns.countplot(x="CNT_CHILDREN", hue="TARGET", data=combined_df, palette="Set1")
plt.legend(loc="upper center")
plt.title("Distribution of Number of children client has by repayment status")

# Second subplot
plt.subplot(212)
sns.countplot(x="CNT_FAM_MEMBERS", hue="TARGET", data=combined_df, palette="Set1")
plt.legend(loc="upper center")
plt.title("Distribution of Number of family members client has by repayment status")

fig.set_facecolor("lightblue")

plt.show()


default = combined_df[combined_df["TARGET"]==1][[ 'NAME_CONTRACT_TYPE_x', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]
non_default = combined_df[combined_df["TARGET"]==0][[ 'NAME_CONTRACT_TYPE_x', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']]

d_cols = ['NAME_CONTRACT_TYPE_x', 'CODE_GENDER','FLAG_OWN_CAR', 'FLAG_OWN_REALTY']
d_length = len(d_cols)

fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
    plt.subplot(1,4,j+1)
    default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism"),startangle = 90,
                                        wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
    circ = plt.Circle((0,0),.7,color="white")
    plt.gca().add_artist(circ)
    plt.ylabel("")
    plt.title(i+"-Defaulter")


fig = plt.figure(figsize=(16,4))
for i,j in itertools.zip_longest(d_cols,range(d_length)):
    plt.subplot(1,4,j+1)
    non_default[i].value_counts().plot.pie(autopct = "%1.0f%%",colors = sns.color_palette("prism",3),startangle = 90,
                                           wedgeprops={"linewidth":1,"edgecolor":"white"},shadow =True)
    circ = plt.Circle((0,0),.7,color="white")
    plt.gca().add_artist(circ)
    plt.ylabel("")
    plt.title(i+"-Repayer")


cols = [ 'AMT_INCOME_TOTAL', 'AMT_CREDIT_x','AMT_ANNUITY_x', 'AMT_GOODS_PRICE_x']
length = len(cols)
cs = ["r","b","g","k"]

ax = plt.figure(figsize=(18,18))
ax.set_facecolor("lightgrey")
for i,j,k in itertools.zip_longest(cols,range(length),cs):
    plt.subplot(2,2,j+1)
    sns.distplot(combined_df[combined_df[i].notnull()][i],color=k)
    plt.axvline(combined_df[i].mean(),label = "mean",linestyle="dashed",color="k")
    plt.legend(loc="best")
    plt.title(i)
    plt.subplots_adjust(hspace = .2)


import itertools
import seaborn as sns
import matplotlib.pyplot as plt

# Compare summary statistics between defaulters and non-defaulters for loan amounts
df = combined_df.groupby("TARGET")[cols].describe().transpose().reset_index()
df = df[df["level_1"].isin([ 'mean', 'std', 'min', 'max'])] 

df_x = df[["level_0","level_1",0]]
df_y = df[["level_0","level_1",1]]
df_x = df_x.rename(columns={'level_0':"amount_type", 'level_1':"statistic", 0:"amount"})
df_x["type"] = "REPAYER"
df_y = df_y.rename(columns={'level_0':"amount_type", 'level_1':"statistic", 1:"amount"})
df_y["type"] = "DEFAULTER"

df_new = pd.concat([df_x, df_y], axis=0)

# Get unique statistics to plot
stat = df_new["statistic"].unique().tolist()
length = len(stat)

plt.figure(figsize=(20, 20))

# Create subplots for each statistic
for i, j in itertools.zip_longest(stat, range(length)):
    plt.subplot(2, 2, j + 1)
    # Use keyword arguments for sns.barplot
    fig = sns.barplot(x="amount_type", y="amount", data=df_new[df_new["statistic"] == i], 
                      hue="type", palette=["g", "r"])
    plt.title(f"{i} -- Defaulters vs Non-defaulters")
    plt.subplots_adjust(hspace=0.4)
    fig.set_facecolor("lightgrey")

plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

# Define the columns for the data
cols = ['AMT_INCOME_TOTAL', 'AMT_CREDIT_x', 'AMT_ANNUITY_x', 'AMT_GOODS_PRICE_x']

# Calculate the mean for each gender group
df1 = combined_df.groupby("CODE_GENDER")[cols].mean().transpose().reset_index()

# Separate data for each gender
df_f = df1[["index", "F"]]
df_f = df_f.rename(columns={'index': "amt_type", 'F': "amount"})
df_f["gender"] = "FEMALE"

df_m = df1[["index", "M"]]
df_m = df_m.rename(columns={'index': "amt_type", 'M': "amount"})
df_m["gender"] = "MALE"

df_xna = df1[["index", "XNA"]]
df_xna = df_xna.rename(columns={'index': "amt_type", 'XNA': "amount"})
df_xna["gender"] = "XNA"

# Combine all gender data into a single DataFrame
df_gen = pd.concat([df_m, df_f, df_xna], axis=0)

# Create the barplot with correct keyword arguments
plt.figure(figsize=(12, 5))
ax = sns.barplot(x="amt_type", y="amount", data=df_gen, hue="gender", palette="Set1")
plt.title("Average Income, Credit, Annuity & Goods Price by Gender")
plt.show()


fig = plt.figure(figsize=(10,8))
plt.scatter(combined_df[combined_df["TARGET"]==0]['AMT_ANNUITY_x'],combined_df[combined_df["TARGET"]==0]['AMT_CREDIT_x'],s=35,
            color="b",alpha=.5,label="REPAYER",linewidth=.5,edgecolor="k")
plt.scatter(combined_df[combined_df["TARGET"]==1]['AMT_ANNUITY_x'],combined_df[combined_df["TARGET"]==1]['AMT_CREDIT_x'],s=35,
            color="r",alpha=.2,label="DEFAULTER",linewidth=.5,edgecolor="k")
plt.legend(loc="best",prop={"size":15})
plt.xlabel("AMT_ANNUITY")
plt.ylabel("AMT_CREDIT")
plt.title("Scatter plot between credit amount and annuity amount")
plt.show()


amt = combined_df[[ 'AMT_INCOME_TOTAL','AMT_CREDIT_x',
                         'AMT_ANNUITY_x', 'AMT_GOODS_PRICE_x',"TARGET"]]
amt = amt[(amt["AMT_GOODS_PRICE_x"].notnull()) & (amt["AMT_ANNUITY_x"].notnull())]
sns.pairplot(amt,hue="TARGET",palette=["b","r"])
plt.show()


plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=combined_df["NAME_TYPE_SUITE_x"],
              palette="Set2",
              order=combined_df["NAME_TYPE_SUITE_x"].value_counts().index[:5])
plt.title("Distribution of Suite type")

plt.subplot(122)
sns.countplot(y=combined_df["NAME_TYPE_SUITE_x"],
              hue=combined_df["CODE_GENDER"],palette="Set2",
              order=combined_df["NAME_TYPE_SUITE_x"].value_counts().index[:5])
plt.ylabel("")
plt.title("Distribution of Suite type by gender")
plt.subplots_adjust(wspace = .4)


plt.figure(figsize=(18,12))
plt.subplot(121)
sns.countplot(y=combined_df["NAME_INCOME_TYPE"],
              palette="Set2",
              order=combined_df["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.title("Distribution of client income type")

plt.subplot(122)
sns.countplot(y=combined_df["NAME_INCOME_TYPE"],
              hue=combined_df["CODE_GENDER"],
              palette="Set2",
              order=combined_df["NAME_INCOME_TYPE"].value_counts().index[:4])
plt.ylabel("")
plt.title("Distribution of client income  type by gender")
plt.subplots_adjust(wspace = .4)


import seaborn as sns
import matplotlib.pyplot as plt

# Group by education type and income type, then calculate the mean income
edu = combined_df.groupby(['NAME_EDUCATION_TYPE', 'NAME_INCOME_TYPE'])['AMT_INCOME_TOTAL'].mean().reset_index().sort_values(by='AMT_INCOME_TOTAL', ascending=False)

# Create the plot
fig = plt.figure(figsize=(13, 7))
ax = sns.barplot(x='NAME_INCOME_TYPE', y='AMT_INCOME_TOTAL', data=edu, hue='NAME_EDUCATION_TYPE', palette="seismic")

# Set the background color and title
ax.set_facecolor("k")
plt.title("Average Earnings by Different Professions and Education Types")

# Show the plot
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt
import itertools

# Replace target values with labels for better readability
df_flag["TARGET"] = df_flag["TARGET"].replace({1: "defaulter", 0: "repayer"})

# Set up the figure with subplots
fig = plt.figure(figsize=(13, 24))
fig.set_facecolor("lightgrey")

# Loop over the columns and create count plots for each document flag
for i, j in itertools.zip_longest(cols, range(len(cols))):
    plt.subplot(5, 4, j + 1)
    ax = sns.countplot(x=df_flag[i], hue=df_flag["TARGET"], palette=["r", "b"])  # Specify x
    plt.yticks(fontsize=5)
    plt.xlabel("")
    plt.title(i)
    ax.set_facecolor("k")

# Show the plot
plt.tight_layout()
plt.show()


import seaborn as sns
import matplotlib.pyplot as plt

# Group by contract type and calculate the mean and sum
mn = combined_df.groupby("NAME_CONTRACT_TYPE_y")[["AMT_APPLICATION","AMT_CREDIT_y"]].mean().stack().reset_index()
tt = combined_df.groupby("NAME_CONTRACT_TYPE_y")[["AMT_APPLICATION","AMT_CREDIT_y"]].sum().stack().reset_index()

# Set up the figure for plotting
fig = plt.figure(figsize=(10, 13))
fig.set_facecolor("ghostwhite")

# Plot average amounts by contract types
plt.subplot(211)
ax = sns.barplot(x="level_1", y=0, data=mn[:6], hue="NAME_CONTRACT_TYPE_y", palette="inferno")
ax.set_facecolor("k")
ax.set_xlabel("Average Amounts")
ax.set_title("Average Amounts by Contract Types")

# Plot total amounts by contract types
plt.subplot(212)
ax1 = sns.barplot(x="level_1", y=0, data=tt[:6], hue="NAME_CONTRACT_TYPE_y", palette="magma")
ax1.set_facecolor("k")
ax1.set_xlabel("Total Amounts")
ax1.set_title("Total Amounts by Contract Types")

# Adjust layout for better spacing
plt.subplots_adjust(hspace=0.2)

# Show the plot
plt.show()


plt.figure(figsize=(14,5))
plt.subplot(121)
combined_df.groupby("NAME_CONTRACT_TYPE_y")["AMT_ANNUITY_y"].sum().plot(kind="bar")
plt.xticks(rotation=0)
plt.title("Total annuity amount by contract types in previous applications")
plt.subplot(122)
combined_df.groupby("NAME_CONTRACT_TYPE_y")["AMT_ANNUITY_y"].mean().plot(kind="bar")
plt.title("average annuity amount by contract types in previous applications")
plt.xticks(rotation=0)
plt.show()


ax = pd.crosstab(combined_df["NAME_CONTRACT_TYPE_y"],combined_df["NAME_CONTRACT_STATUS"]).plot(kind="barh",figsize=(10,7),stacked=True)
plt.xticks(rotation =0)
plt.ylabel("count")
plt.title("Count of application status by application type")
ax.set_facecolor("k")


import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import itertools

# Creating the percentage values for different contract statuses
purpose = pd.crosstab(combined_df["NAME_CASH_LOAN_PURPOSE"], combined_df["NAME_CONTRACT_STATUS"])

# Calculating the percentage for each contract status type
purpose["a"] = (purpose["Approved"] * 100) / (purpose["Approved"] + purpose["Canceled"] + purpose["Refused"] + purpose["Unused offer"])
purpose["c"] = (purpose["Canceled"] * 100) / (purpose["Approved"] + purpose["Canceled"] + purpose["Refused"] + purpose["Unused offer"])
purpose["r"] = (purpose["Refused"] * 100) / (purpose["Approved"] + purpose["Canceled"] + purpose["Refused"] + purpose["Unused offer"])
purpose["u"] = (purpose["Unused offer"] * 100) / (purpose["Approved"] + purpose["Canceled"] + purpose["Refused"] + purpose["Unused offer"])

# Selecting the relevant columns and stacking the data
purpose_new = purpose[["a", "c", "r", "u"]]
purpose_new = purpose_new.stack().reset_index()
purpose_new["NAME_CONTRACT_STATUS"] = purpose_new["NAME_CONTRACT_STATUS"].replace({
    "a": "accepted_percentage",
    "c": "cancelled_percentage",
    "r": "refused_percentage",
    "u": "unused_percentage"
})

# Preparing for plotting
lst = purpose_new["NAME_CONTRACT_STATUS"].unique().tolist()
length = len(lst)
cs = ["lime", "orange", "r", "b"]

# Plotting the data
fig = plt.figure(figsize=(14, 18))
fig.set_facecolor("lightgrey")

# Loop through the unique contract statuses and plot the data
for i, j, k in itertools.zip_longest(lst, range(length), cs):
    plt.subplot(2, 2, j + 1)
    dat = purpose_new[purpose_new["NAME_CONTRACT_STATUS"] == i]
    ax = sns.barplot(x=0, y="NAME_CASH_LOAN_PURPOSE", data=dat.sort_values(by=0, ascending=False), color=k)
    plt.ylabel("")
    plt.xlabel("percentage")
    plt.title(f"{i} by purpose")
    plt.subplots_adjust(wspace=0.7)
    ax.set_facecolor("k")

plt.show()


cols = ['DAYS_FIRST_DRAWING', 'DAYS_FIRST_DUE', 'DAYS_LAST_DUE_1ST_VERSION','DAYS_LAST_DUE', 'DAYS_TERMINATION']
plt.figure(figsize=(12,6))
sns.heatmap(combined_df[cols].describe()[1:].transpose(),
            annot=True,linewidth=2,linecolor="k",cmap=sns.color_palette("inferno"))
plt.show()


df_repayer = combined_df[combined_df['TARGET'] == 0]
df_defaulter = combined_df[combined_df['TARGET'] == 1]


# Select only numeric columns from the DataFrame
df_repayer_numeric = df_repayer.select_dtypes(include=[np.number])

# Compute the correlation matrix
corrmat = df_repayer_numeric.corr()

# Mask the upper triangle of the correlation matrix (to avoid redundant values)
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))

# Unstack the correlation matrix and reset the index to make it easier to work with
corrdf = corrdf.unstack().reset_index()

# Rename the columns for clarity
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop rows where the correlation is NaN
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values and take the absolute value
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort the correlations in descending order and show the top 10
corrdf.sort_values(by='Correlation', ascending=False).head(10)


# Select only numeric columns from the df_defaulter DataFrame
df_defaulter_numeric = df_defaulter.select_dtypes(include=[np.number])

# Compute the correlation matrix for the numeric columns
corrmat = df_defaulter_numeric.corr()

# Mask the upper triangle of the correlation matrix (to avoid redundant values)
corrdf = corrmat.where(np.triu(np.ones(corrmat.shape), k=1).astype(bool))

# Unstack the correlation matrix and reset the index to make it easier to work with
corrdf = corrdf.unstack().reset_index()

# Rename the columns for clarity
corrdf.columns = ['Var1', 'Var2', 'Correlation']

# Drop rows where the correlation is NaN
corrdf.dropna(subset=['Correlation'], inplace=True)

# Round the correlation values and take the absolute value
corrdf['Correlation'] = round(corrdf['Correlation'], 2)
corrdf['Correlation'] = abs(corrdf['Correlation'])

# Sort the correlations in descending order and show the top 10
corrdf.sort_values(by='Correlation', ascending=False).head(10)

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	104582.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307509.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	134133.000000	3.068510e+05	246546.000000	151450.00000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	159080.000000	306490.000000	306490.000000	306490.000000	306490.000000	307510.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.00000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	-4986.120328	-2994.202373	12.061091	0.999997	0.819889	0.199368	0.998133	0.281066	0.056720	2.152665	2.052463	2.031521	12.063419	0.015144	0.050769	0.040659	0.078173	0.230454	0.179555	0.502130	5.143927e-01	0.510853	0.11744	0.088442	0.977735	0.752471	0.044621	0.078942	0.149725	0.226282	0.231894	0.066333	0.100775	0.107399	0.008809	0.028358	0.114231	0.087543	0.977065	0.759637	0.042553	0.074490	0.145193	0.222315	0.228058	0.064958	0.105645	0.105975	0.008076	0.027022	0.117850	0.087955	0.977752	0.755746	0.044595	0.078078	0.149213	0.225897	0.231625	0.067169	0.101954	0.108607	0.008651	0.028236	0.102547	1.422245	0.143421	1.405292	0.100049	-962.858788	0.000042	0.710023	0.000081	0.015115	0.088055	0.000192	0.081376	0.003896	0.000023	0.003912	0.000007	0.003525	0.002936	0.00121	0.009928	0.000267	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	3522.886321	1509.450419	11.944812	0.001803	0.384280	0.399526	0.043164	0.449521	0.231307	0.910682	0.509034	0.502737	3.265832	0.122126	0.219526	0.197499	0.268444	0.421124	0.383817	0.211062	1.910602e-01	0.194844	0.10824	0.082438	0.059223	0.113280	0.076036	0.134576	0.100049	0.144641	0.161380	0.081184	0.092576	0.110565	0.047732	0.069523	0.107936	0.084307	0.064575	0.110111	0.074445	0.132256	0.100977	0.143709	0.161160	0.081750	0.097880	0.111845	0.046276	0.070254	0.109076	0.082179	0.059897	0.112066	0.076144	0.134467	0.100368	0.145067	0.161934	0.082167	0.093642	0.112260	0.047415	0.070166	0.107462	2.400989	0.446698	2.379803	0.362291	826.808487	0.006502	0.453752	0.009016	0.122010	0.283376	0.013850	0.273412	0.062295	0.004771	0.062424	0.002550	0.059268	0.054110	0.03476	0.099144	0.016327	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	-24672.000000	-7197.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014568	8.173617e-08	0.000527	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-4292.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	-7479.500000	-4299.000000	5.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.334007	3.924574e-01	0.370650	0.05770	0.044200	0.976700	0.687200	0.007800	0.000000	0.069000	0.166700	0.083300	0.018700	0.050400	0.045300	0.000000	0.000000	0.052500	0.040700	0.976700	0.699400	0.007200	0.000000	0.069000	0.166700	0.083300	0.016600	0.054200	0.042700	0.000000	0.000000	0.058300	0.043700	0.976700	0.691400	0.007900	0.000000	0.069000	0.166700	0.083300	0.018700	0.051300	0.045700	0.000000	0.000000	0.041200	0.000000	0.000000	0.000000	0.000000	-1570.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	-4504.000000	-3254.000000	9.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	12.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.505998	5.659614e-01	0.535276	0.08760	0.076300	0.981600	0.755200	0.021100	0.000000	0.137900	0.166700	0.208300	0.048100	0.075600	0.074500	0.000000	0.003600	0.084000	0.074600	0.981600	0.764800	0.019000	0.000000	0.137900	0.166700	0.208300	0.045800	0.077100	0.073100	0.000000	0.001100	0.086400	0.075800	0.981600	0.758500	0.020800	0.000000	0.137900	0.166700	0.208300	0.048700	0.076100	0.074900	0.000000	0.003100	0.068800	0.000000	0.000000	0.000000	0.000000	-757.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	-2010.000000	-1720.000000	15.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	3.000000	2.000000	2.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.675053	6.636171e-01	0.669057	0.14850	0.112200	0.986600	0.823200	0.051500	0.120000	0.206900	0.333300	0.375000	0.085600	0.121000	0.129900	0.003900	0.027700	0.143900	0.112400	0.986600	0.823600	0.049000	0.120800	0.206900	0.333300	0.375000	0.084100	0.131300	0.125200	0.003900	0.023100	0.148900	0.111600	0.986600	0.825600	0.051300	0.120000	0.206900	0.333300	0.375000	0.086800	0.123100	0.130300	0.003900	0.026600	0.127600	2.000000	0.000000	2.000000	0.000000	-274.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	0.000000	0.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	20.000000	3.000000	3.000000	23.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.962693	8.549997e-01	0.896010	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	348.000000	34.000000	344.000000	24.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1.670214e+06	1.670214e+06	1.297979e+06	1.670214e+06	1.670213e+06	7.743700e+05	1.284699e+06	1.670214e+06	1.670214e+06	774370.000000	5951.000000	5951.000000	1.670214e+06	1.670214e+06	1.297984e+06	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000
mean	1.923089e+06	2.783572e+05	1.595512e+04	1.752339e+05	1.961140e+05	6.697402e+03	2.278473e+05	1.248418e+01	9.964675e-01	0.079637	0.188357	0.773503	-8.806797e+02	3.139511e+02	1.605408e+01	342209.855039	13826.269337	33767.774054	76582.403064	81992.343838	0.332570
std	5.325980e+05	1.028148e+05	1.478214e+04	2.927798e+05	3.185746e+05	2.092150e+04	3.153966e+05	3.334028e+00	5.932963e-02	0.107823	0.087671	0.100879	7.790997e+02	7.127443e+03	1.456729e+01	88916.115834	72444.869708	106857.034789	149647.415123	153303.516729	0.471134
min	1.000001e+06	1.000010e+05	0.000000e+00	0.000000e+00	0.000000e+00	-9.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	-0.000015	0.034781	0.373150	-2.922000e+03	-1.000000e+00	0.000000e+00	-2922.000000	-2892.000000	-2801.000000	-2889.000000	-2874.000000	0.000000
25%	1.461857e+06	1.893290e+05	6.321780e+03	1.872000e+04	2.416050e+04	0.000000e+00	5.084100e+04	1.000000e+01	1.000000e+00	0.000000	0.160716	0.715645	-1.300000e+03	-1.000000e+00	6.000000e+00	365243.000000	-1628.000000	-1242.000000	-1314.000000	-1270.000000	0.000000
50%	1.923110e+06	2.787145e+05	1.125000e+04	7.104600e+04	8.054100e+04	1.638000e+03	1.123200e+05	1.200000e+01	1.000000e+00	0.051605	0.189122	0.835095	-5.810000e+02	3.000000e+00	1.200000e+01	365243.000000	-831.000000	-361.000000	-537.000000	-499.000000	0.000000
75%	2.384280e+06	3.675140e+05	2.065842e+04	1.803600e+05	2.164185e+05	7.740000e+03	2.340000e+05	1.500000e+01	1.000000e+00	0.108909	0.193330	0.852537	-2.800000e+02	8.200000e+01	2.400000e+01	365243.000000	-411.000000	129.000000	-74.000000	-44.000000	1.000000
max	2.845382e+06	4.562550e+05	4.180581e+05	6.905160e+06	6.905160e+06	3.060045e+06	6.905160e+06	2.300000e+01	1.000000e+00	1.000000	1.000000	1.000000	-1.000000e+00	4.000000e+06	8.400000e+01	365243.000000	365243.000000	365243.000000	365243.000000	365243.000000	1.000000

	TARGET	count
SK_ID_CURR
100002	1	1
100003	0	3
100004	0	1
100006	0	9
100007	0	6
100008	0	5
100009	0	7
100010	0	1
100011	0	4
100012	0	4

	TARGET	count
SK_ID_CURR
100002	1	1
333349	1	1
333587	1	1
333582	1	1
333534	1	1
333506	1	1
333419	1	1
333355	1	1
333337	1	1
334761	1	1

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE_x	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT_x	AMT_ANNUITY_x	AMT_GOODS_PRICE_x	NAME_TYPE_SUITE_x	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START_x	HOUR_APPR_PROCESS_START_x	ORGANIZATION_TYPE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	SK_ID_PREV	NAME_CONTRACT_TYPE_y	AMT_ANNUITY_y	AMT_APPLICATION	AMT_CREDIT_y	AMT_GOODS_PRICE_y	WEEKDAY_APPR_PROCESS_START_y	HOUR_APPR_PROCESS_START_y	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE_y	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	351000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	-9461	-637	-3648.0	-2120	1	1	0	1	1	1.0	2	2	WEDNESDAY	10	Business Entity Type 3	2.0	2.0	2.0	2.0	-1134.0	1	0.0	0.0	0.0	0.0	0.0	1.0	1038818	Consumer loans	9251.775	179055.0	179055.0	179055.0	SATURDAY	9	Y	1	XAP	Approved	-606	XNA	XAP	NaN	New	Vehicles	POS	XNA	Stone	500	Auto technology	24.0	low_normal	POS other with interest	365243.0	-565.0	125.0	-25.0	-17.0	0.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	1	1	0	1	1	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	0.0	0.0	0.0	0.0	0.0	1810518	Cash loans	98356.995	900000.0	1035882.0	900000.0	FRIDAY	12	Y	1	XNA	Approved	-746	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	low_normal	Cash X-Sell: low	365243.0	-716.0	-386.0	-536.0	-527.0	1.0
3	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	1	1	0	1	1	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	0.0	0.0	0.0	0.0	0.0	2396755	Consumer loans	6737.310	68809.5	68053.5	68809.5	SATURDAY	15	Y	1	XAP	Approved	-2341	Cash through the bank	XAP	Family	Refreshed	Consumer Electronics	POS	XNA	Country-wide	200	Consumer electronics	12.0	middle	POS household with interest	365243.0	-2310.0	-1980.0	-1980.0	-1976.0	1.0
2	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	1	1	0	1	1	2.0	1	1	MONDAY	11	School	1.0	0.0	1.0	0.0	-828.0	1	0.0	0.0	0.0	0.0	0.0	0.0	2636178	Consumer loans	64567.665	337500.0	348637.5	337500.0	SUNDAY	17	Y	1	XAP	Approved	-828	Cash through the bank	XAP	Family	Refreshed	Furniture	POS	XNA	Stone	1400	Furniture	6.0	middle	POS industry with interest	365243.0	-797.0	-647.0	-647.0	-639.0	0.0
4	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	135000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	-19046	-225	-4260.0	-2531	1	1	1	1	1	1.0	2	2	MONDAY	9	Government	0.0	0.0	0.0	0.0	-815.0	0	0.0	0.0	0.0	0.0	0.0	0.0	1564014	Consumer loans	5357.250	24282.0	20106.0	24282.0	FRIDAY	5	Y	1	XAP	Approved	-815	Cash through the bank	XAP	Unaccompanied	New	Mobile	POS	XNA	Regional / Local	30	Connectivity	4.0	middle	POS mobile without interest	365243.0	-784.0	-694.0	-724.0	-714.0	0.0
9	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	1	1	0	1	0	2.0	2	2	WEDNESDAY	17	Business Entity Type 3	2.0	0.0	2.0	0.0	-617.0	1	NaN	NaN	NaN	NaN	NaN	NaN	1020698	Cash loans	39954.510	454500.0	481495.5	454500.0	SATURDAY	12	Y	1	XNA	Approved	-438	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	18.0	high	Cash X-Sell: high	NaN	NaN	NaN	NaN	NaN	NaN
10	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	1	1	0	1	0	2.0	2	2	WEDNESDAY	17	Business Entity Type 3	2.0	0.0	2.0	0.0	-617.0	1	NaN	NaN	NaN	NaN	NaN	NaN	1243599	Cash loans	NaN	0.0	0.0	NaN	THURSDAY	15	Y	1	XNA	Canceled	-181	XNA	XAP	NaN	Repeater	XNA	XNA	XNA	Credit and cash offices	-1	XNA	NaN	XNA	Cash	NaN	NaN	NaN	NaN	NaN	NaN
8	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	1	1	0	1	0	2.0	2	2	WEDNESDAY	17	Business Entity Type 3	2.0	0.0	2.0	0.0	-617.0	1	NaN	NaN	NaN	NaN	NaN	NaN	1489396	Revolving loans	13500.000	270000.0	270000.0	270000.0	THURSDAY	15	Y	1	XAP	Approved	-181	XNA	XAP	Unaccompanied	Repeater	XNA	Cards	x-sell	Credit and cash offices	-1	XNA	0.0	XNA	Card X-Sell	365243.0	365243.0	365243.0	365243.0	365243.0	0.0
13	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	1	1	0	1	0	2.0	2	2	WEDNESDAY	17	Business Entity Type 3	2.0	0.0	2.0	0.0	-617.0	1	NaN	NaN	NaN	NaN	NaN	NaN	1697039	Cash loans	32696.100	688500.0	906615.0	688500.0	THURSDAY	15	Y	1	XNA	Refused	-181	Cash through the bank	LIMIT	Unaccompanied	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	48.0	low_normal	Cash X-Sell: low	NaN	NaN	NaN	NaN	NaN	NaN
5	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	1	1	0	1	0	2.0	2	2	WEDNESDAY	17	Business Entity Type 3	2.0	0.0	2.0	0.0	-617.0	1	NaN	NaN	NaN	NaN	NaN	NaN	2078043	Cash loans	24246.000	675000.0	675000.0	675000.0	THURSDAY	15	Y	1	XNA	Approved	-181	Cash through the bank	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	48.0	low_normal	Cash X-Sell: low	365243.0	-151.0	1259.0	-151.0	-143.0	0.0

Sama Amr Habib¶

Risk Analytics In Banking & Financial Services 1¶

Detailed analysis (EDA) on application_data to identify the important features which help us to identify the defaulters¶

Import Libraries¶

Explore data¶

1. 'application_data.csv'¶

2. 'previous_application.csv'¶

3. 'columns_description.csv'¶

Data Preprocessing¶

Previous Application data¶

Removing columns with missing values more than 50%¶

key point¶

Separating numerical and categorical columns from previous_application¶

Application data¶

Removing columns with missing values more than 40%¶

Converting negative values to absolute values¶

Concatenating application_data and previous_application¶

Distribution in Contract types in application_data¶¶

Distribution of amount data¶

Comparing summary statistics between defaulters and non - defaulters for loan amounts.¶

Income of client -¶¶

Pair Plot between amount variables¶

Distribution of Suite type¶

Distribution of client income type¶

Corelation between variables¶

Application Data¶

Previous Application¶

Application Data¶

Top 10 Correlation Fields for Repayer¶

2nd contains data where we inner join the records (application_data, previous_application) with same the SK_ID_CURR¶

Combine data¶

Converting negative values to absolute values¶

Corelation between variables¶

Top 10 Correlation Fields for Repayer¶

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	Y	1	0.0	0.182832	0.867336	XAP	Approved	-73	Cash through the bank	XAP	NaN	Repeater	Mobile	POS	XNA	Country-wide	35	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-164	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Contact center	-1	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-301	Cash through the bank	XAP	Spouse, partner	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	Y	1	NaN	NaN	NaN	XNA	Approved	-512	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	Y	1	NaN	NaN	NaN	Repairs	Refused	-781	Cash through the bank	HC	NaN	Repeater	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	Unnamed: 0	Table	Row	Description	Special
0	1	application_data	SK_ID_CURR	ID of loan in our sample	NaN
1	2	application_data	TARGET	Target variable (1 - client with payment diffi...	NaN
2	5	application_data	NAME_CONTRACT_TYPE	Identification if loan is cash or revolving	NaN
3	6	application_data	CODE_GENDER	Gender of the client	NaN
4	7	application_data	FLAG_OWN_CAR	Flag if the client owns a car	NaN

	Unnamed: 0
count	160.00000
mean	94.58750
std	64.12727
min	1.00000
25%	42.75000
50%	82.50000
75%	122.25000
max	213.00000

	TARGET	count
SK_ID_CURR
265681	0	73
173680	0	72
242412	0	68
206783	0	67
389950	0	64
382179	0	64
198355	0	63
345161	0	62
446486	0	62
238250	0	61

	Var1	Var2	Correlation
2278	OBS_60_CNT_SOCIAL_CIRCLE	OBS_30_CNT_SOCIAL_CIRCLE	1.00
4786	AMT_GOODS_PRICE_y	AMT_APPLICATION	1.00
984	FLAG_EMP_PHONE	DAYS_EMPLOYED	1.00
4787	AMT_GOODS_PRICE_y	AMT_CREDIT_y	0.99
454	AMT_GOODS_PRICE_x	AMT_CREDIT_x	0.99
4711	AMT_CREDIT_y	AMT_APPLICATION	0.97
1519	REGION_RATING_CLIENT_W_CITY	REGION_RATING_CLIENT	0.95
5547	DAYS_TERMINATION	DAYS_LAST_DUE	0.93
1352	CNT_FAM_MEMBERS	CNT_CHILDREN	0.88
1823	LIVE_REGION_NOT_WORK_REGION	REG_REGION_NOT_WORK_REGION	0.87